Device capability discovery method and wireless communication device

Abstract

The disclosure provides a device capability discovery method and a wireless communication device. The wireless communication device transmits a capability message of the wireless communication device to a source device having a pool of machine learning (ML) models. The capability message shows whether the wireless communication device is capable of executing multiple ML models. The wireless communication device downloads if needed, and activates one or more ML models from a subset in the pool of ML models. The subset in the pool of ML models matches the capability message of the wireless communication device.

1 . A device capability discovery method for machine learning (ML), executable in a wireless communication device, comprising:
 transmitting a capability message of the wireless communication device to a source device having machine learning (ML) models, wherein the capability message shows whether the wireless communication device is capable of executing multiple ML models; and   downloading and activating one or more ML models from a subset of ML models, wherein the subset of ML models matches the capability message of the wireless communication device.   
     
     
         2 . The method of  claim 1 , wherein the capability message comprises a model type. 
     
     
         3 . The method of  claim 2 , wherein the model type comprises one or more of:
 a type of ML model trained to provide CSI feedback;   a type of ML model trained to provide beam prediction in a time domain;   a type of ML model trained to provide beam prediction in a spatial domain; and   a type of ML model trained to provide positioning.   
     
     
         4 . The method of  claim 2 , wherein the model type comprises one or more of:
 a type of generalized ML model; and   a type of scenario-specific ML model.   
     
     
         5 . The method of  claim 4 , wherein a first range of ML model identifiers is allocated to the type of generalized ML model, and a second range of ML model identifiers is allocated to the type of scenario-specific ML model. 
     
     
         6 . The method of  claim 4 , wherein according to a mapping between a first generalized ML model and a first scenario-specific ML model, the first scenario-specific ML model serves as a backup ML model for the first generalized ML model, the first scenario-specific ML model is activated in response to deactivation of the first generalized ML model. 
     
     
         7 . The method of  claim 6 , wherein the mapping between the first generalized ML model and the first scenario-specific ML model is determined based on association between a model identifier of the first generalized ML model and a model identifier of the first scenario-specific ML model. 
     
     
         8 . The method of  claim 6 , wherein the mapping between the first generalized ML model and the first scenario-specific ML model is determined based on association between a parameter range of the first generalized ML model and a parameter range of the first scenario-specific ML model. 
     
     
         9 . The method of  claim 8 , wherein the first scenario-specific ML model works for a first parameter range, a second scenario-specific ML model works for a second parameter range, the first generalized ML model works for the first parameter range and the second parameter range, and the first scenario-specific ML model and the second scenario-specific ML model serve as backup ML models for the first generalized ML model. 
     
     
         10 . The method of  claim 2 , wherein the capability message comprises a maximum number of ML models supported by the wireless communication device. 
     
     
         11 . The method of  claim 10 , wherein the maximum number of ML models supported by the wireless communication device is associated with the model type. 
     
     
         12 . The method of  claim 1 , wherein the capability message comprises a set of capabilities of the wireless communication device for determining a complexity level of the wireless communication device; or
 the capability message comprises the complexity level of the wireless communication device.   
     
     
         13 . The method of  claim 12 , wherein the complexity level of the wireless communication device is associated with the model type. 
     
     
         14 . The method of  claim 12 , further comprising:
 reporting successful deployment of a first download ML model in the downloaded one or more ML models when a complexity level of the first download ML model matches the complexity level of the wireless communication device; and   reporting unsuccessful deployment of the first download ML model in the downloaded one or more ML models when the complexity level of the first download ML model does not match matches the complexity level of the wireless communication device.   
     
     
         15 . The method of  claim 12 , wherein the set of capabilities of the wireless communication device comprises one or more of:
 a central processing unit (CPU), a memory size, a storage, floating-point operations per second (FLOPs), power, a buffer size, and a bus bandwidth of the wireless communication device.   
     
     
         16 . (canceled) 
     
     
         17 . (canceled) 
     
     
         18 . The method of  claim 1 , wherein a first set of UE capabilities of the wireless communication device is disabled in response to enabling of a second set of UE capabilities of the wireless communication device; or
 the first set of UE capabilities of the wireless communication device is enabled in response to enabling of the second set of UE capabilities of the wireless communication device.   
     
     
         19 - 29 . (canceled) 
     
     
         30 . A device capability discovery method for machine learning (ML),
 executable in a wireless communication device, comprising:   transmitting a capability message of the wireless communication device to a source device, the wireless communication device has machine learning (ML) models, wherein the capability message shows whether the wireless communication device is capable of executing multiple ML models;   receiving an indication showing a subset of ML models, wherein the subset of ML models matches the capability message of the wireless communication device; and   activating one or more ML models deployed in the wireless communication device in response to the subset of ML models.   
     
     
         31 - 53 . (canceled) 
     
     
         54 . A wireless communication device comprising:
 a processor, configured to call and run a computer program stored in a memory, to cause a device in which the processor is installed to execute the method of  claim 1 .   
     
     
         55 . A chip, comprising:
 a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute the method of  claim 1 .   
     
     
         56 . A computer-readable storage medium, in which a computer program is stored, wherein the computer program causes a computer to execute the method of  claim 1 . 
     
     
         57 - 58 . (canceled)